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The prediction of a pathogenesis-related secretome of Puccinia helianthi through high-throughput transcriptome analysis

BACKGROUND: Many plant pathogen secretory proteins are known to be elicitors or pathogenic factors,which play an important role in the host-pathogen interaction process. Bioinformatics approaches make possible the large scale prediction and analysis of secretory proteins from the Puccinia helianthi...

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Detalles Bibliográficos
Autores principales: Jing, Lan, Guo, Dandan, Hu, Wenjie, Niu, Xiaofan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5346188/
https://www.ncbi.nlm.nih.gov/pubmed/28284182
http://dx.doi.org/10.1186/s12859-017-1577-0
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author Jing, Lan
Guo, Dandan
Hu, Wenjie
Niu, Xiaofan
author_facet Jing, Lan
Guo, Dandan
Hu, Wenjie
Niu, Xiaofan
author_sort Jing, Lan
collection PubMed
description BACKGROUND: Many plant pathogen secretory proteins are known to be elicitors or pathogenic factors,which play an important role in the host-pathogen interaction process. Bioinformatics approaches make possible the large scale prediction and analysis of secretory proteins from the Puccinia helianthi transcriptome. The internet-based software SignalP v4.1, TargetP v1.01, Big-PI predictor, TMHMM v2.0 and ProtComp v9.0 were utilized to predict the signal peptides and the signal peptide-dependent secreted proteins among the 35,286 ORFs of the P. helianthi transcriptome. RESULTS: 908 ORFs (accounting for 2.6% of the total proteins) were identified as putative secretory proteins containing signal peptides. The length of the majority of proteins ranged from 51 to 300 amino acids (aa), while the signal peptides were from 18 to 20 aa long. Signal peptidase I (SpI) cleavage sites were found in 463 of these putative secretory signal peptides. 55 proteins contained the lipoprotein signal peptide recognition site of signal peptidase II (SpII). Out of 908 secretory proteins, 581 (63.8%) have functions related to signal recognition and transduction, metabolism, transport and catabolism. Additionally, 143 putative secretory proteins were categorized into 27 functional groups based on Gene Ontology terms, including 14 groups in biological process, seven in cellular component, and six in molecular function. Gene ontology analysis of the secretory proteins revealed an enrichment of hydrolase activity. Pathway associations were established for 82 (9.0%) secretory proteins. A number of cell wall degrading enzymes and three homologous proteins specific to Phytophthora sojae effectors were also identified, which may be involved in the pathogenicity of the sunflower rust pathogen. CONCLUSIONS: This investigation proposes a new approach for identifying elicitors and pathogenic factors. The eventual identification and characterization of 908 extracellularly secreted proteins will advance our understanding of the molecular mechanisms of interactions between sunflower and rust pathogen and will enhance our ability to intervene in disease states. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-017-1577-0) contains supplementary material, which is available to authorized users.
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spelling pubmed-53461882017-03-14 The prediction of a pathogenesis-related secretome of Puccinia helianthi through high-throughput transcriptome analysis Jing, Lan Guo, Dandan Hu, Wenjie Niu, Xiaofan BMC Bioinformatics Research Article BACKGROUND: Many plant pathogen secretory proteins are known to be elicitors or pathogenic factors,which play an important role in the host-pathogen interaction process. Bioinformatics approaches make possible the large scale prediction and analysis of secretory proteins from the Puccinia helianthi transcriptome. The internet-based software SignalP v4.1, TargetP v1.01, Big-PI predictor, TMHMM v2.0 and ProtComp v9.0 were utilized to predict the signal peptides and the signal peptide-dependent secreted proteins among the 35,286 ORFs of the P. helianthi transcriptome. RESULTS: 908 ORFs (accounting for 2.6% of the total proteins) were identified as putative secretory proteins containing signal peptides. The length of the majority of proteins ranged from 51 to 300 amino acids (aa), while the signal peptides were from 18 to 20 aa long. Signal peptidase I (SpI) cleavage sites were found in 463 of these putative secretory signal peptides. 55 proteins contained the lipoprotein signal peptide recognition site of signal peptidase II (SpII). Out of 908 secretory proteins, 581 (63.8%) have functions related to signal recognition and transduction, metabolism, transport and catabolism. Additionally, 143 putative secretory proteins were categorized into 27 functional groups based on Gene Ontology terms, including 14 groups in biological process, seven in cellular component, and six in molecular function. Gene ontology analysis of the secretory proteins revealed an enrichment of hydrolase activity. Pathway associations were established for 82 (9.0%) secretory proteins. A number of cell wall degrading enzymes and three homologous proteins specific to Phytophthora sojae effectors were also identified, which may be involved in the pathogenicity of the sunflower rust pathogen. CONCLUSIONS: This investigation proposes a new approach for identifying elicitors and pathogenic factors. The eventual identification and characterization of 908 extracellularly secreted proteins will advance our understanding of the molecular mechanisms of interactions between sunflower and rust pathogen and will enhance our ability to intervene in disease states. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-017-1577-0) contains supplementary material, which is available to authorized users. BioMed Central 2017-03-11 /pmc/articles/PMC5346188/ /pubmed/28284182 http://dx.doi.org/10.1186/s12859-017-1577-0 Text en © The Author(s). 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Jing, Lan
Guo, Dandan
Hu, Wenjie
Niu, Xiaofan
The prediction of a pathogenesis-related secretome of Puccinia helianthi through high-throughput transcriptome analysis
title The prediction of a pathogenesis-related secretome of Puccinia helianthi through high-throughput transcriptome analysis
title_full The prediction of a pathogenesis-related secretome of Puccinia helianthi through high-throughput transcriptome analysis
title_fullStr The prediction of a pathogenesis-related secretome of Puccinia helianthi through high-throughput transcriptome analysis
title_full_unstemmed The prediction of a pathogenesis-related secretome of Puccinia helianthi through high-throughput transcriptome analysis
title_short The prediction of a pathogenesis-related secretome of Puccinia helianthi through high-throughput transcriptome analysis
title_sort prediction of a pathogenesis-related secretome of puccinia helianthi through high-throughput transcriptome analysis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5346188/
https://www.ncbi.nlm.nih.gov/pubmed/28284182
http://dx.doi.org/10.1186/s12859-017-1577-0
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